99 95 90 80 70 60 50 40 30 20 10 5 1 Standardized Residual Percent Normal

# 99 95 90 80 70 60 50 40 30 20 10 5 1 standardized

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99 95 90 80 70 60 50 40 30 20 10 5 1 Standardized Residual Percent Normal Probability Plot (response is Price (\$)) 60 59 58 57 56 55 54 53 52 2 1 0 -1 -2 Fitted Value Standardized Residual Versus Fits (response is Score)
3 2 1 0 -1 -2 -3 99 95 90 80 70 60 50 40 30 20 10 5 1 Standardized Residual Percent Normal Probability Plot (response is Score) 7.0 6.5 6.0 5.5 5.0 4.5 4.0 65 60 55 50 45 40 Weight (oz.) Score Scatterplot of Score vs Weight (oz.)
16 15 14 13 12 11 10 65 60 55 50 45 40 Megapixels Score Scatterplot of Score vs Megapixels 400 350 300 250 200 150 100 70 65 60 55 50 45 40 Price (\$) Score Scatterplot of Score vs Price (\$) Results The results show that there is little correlation between a cameras megapixels and weight affecting the price and score of the camera. R 2 is a measure of how close the data is to the fitted
line of regression, or how much the model explains or doesn’t explain the variability of the response data around its mean. The resulting r 2 for the regression analysis for price (\$) versus megapixels and weight was 16.68%. The r 2 for the regression analysis for score versus megapixels and weight resulted in 8.41%. These regression scores show that very little of the variability in price and score are explained by the predictors (weight and megapixels). These values show that price and score are influenced very little to almost none by megapixels and weight. Also, the Standard error of regression (S) shows that for price versus Megapixels and weight, the average distance from the line of regression is 78.54. For Score versus megapixels and weight, the average distance from the line of regression is 6.65. S shows how wrong, on average, the regression model is using the response variable. Smaller values are better because they are closer to the fitted line. Price seems to be the best overall predictor of score because the scatterplots show that there is little deviation from the line of regression, resulting in a much higher r 2 value than any other variable assessed in the study. Score is influenced by price, which can be seen in the regression analysis. It shows very little departure from the line of regression. Regression Analysis: Score versus Price (\$) Analysis of Variance Source DF Adj SS Adj MS F-Value P-Value Regression 1 311.66 311.660 23.80 0.000 Price (\$) 1 311.66 311.660 23.80 0.000 Error 11 144.03 13.094 Lack-of-Fit 6 104.78 17.464 2.22 0.199 Pure Error 5 39.25 7.850 Total 12 455.69 Model Summary S R-sq R-sq(adj) R-sq(pred) 3.61854 68.39% 65.52% 53.13% Coefficients Term Coef SE Coef T-Value P-Value VIF Constant 47.29 2.57 18.38 0.000 Price (\$) 0.0665 0.0136 4.88 0.000 1.00 Regression Equation Score = 47.29 + 0.0665 Price (\$) Fits and Diagnostics for Unusual Observations Obs Score Fit Resid Std Resid 13 46.00 53.27 -7.27 -2.21 R R Large residual
Regression Analysis: Score versus Price (\$) Analysis of Variance Source DF Adj SS Adj MS F-Value P-Value Regression 1 311.66 311.660 23.80 0.000 Price (\$) 1 311.66 311.660